The Virtual Customer - Massachusetts Institute of...

The Virtual Customer

Ely Dahan and John R. Hauser

September 2001

Ely Dahan is an Assistant Professor of Marketing and Management Science, E56-323, Sloan School of Management, Massachusetts Institute of Technology, 38 Memorial Drive, Cambridge, MA 02142-1307, (617) 253-0492, [email protected].

John Hauser is the Kirin Professor of Marketing and Management Science, E56-314, Sloan School of Management, Massachusetts Institute of Technology, 38 Memorial Drive, Cambridge, MA 02142-1307, (617) 253-2929, [email protected].

The authors thank the MIT Center for Innovation in Product Development and the MIT Center for eBusiness for financial support. The authors also wish to thank Nicholas Chan, Wendell Gil-land, Rob Hardy, Adlar Kim, Leonard Lee, Bryant Lin, Meghan McArdle, Olivier Toubia and Limor Weisberg for their contributions and for communicating concepts using computers. Profs. Andrew Lo, Tomaso Poggio, Dražen Prelec, Duncan Simester, and V. Seenu Srinivasan were key contributors to some of the methods described herein. This article has benefited from semi-nars at Columbia University, Georgetown University, Harvard University, M.I.T., University of Texas at Austin, and the Wharton School as well as presentations at the CIPD Spring Research Review, the Epoch Foundation Workshop, the Center for e-Business at MIT, the MIT ILP Sym-posium on “Managing Corporate Innovation,” the AMA Advanced Research Techniques A/R/T Forum in Amelia Island, Florida, and the Marketing Science Conference in Wiesbaden, Ger-many. Demonstrations of the methods discussed in this article, open-source software to imple-ment the methods, and many of the working papers referenced in this article are available at mitsloan.mit.edu/vc.


Author Biographies

Ely Dahan is Assistant Professor of Management Science at MIT's Sloan School of Man-agement. His article with V. Seenu Srinivasan on Internet Concept Testing won the Thomas P. Hustad Best Paper award in the Journal for Product Innovation Management. He completed his PhD in the Operations & IT program at Stanford Business School, where he was a Department of Energy Fellow, an AACSB Doctoral Fellow, and a recipient of the Jaedicke Fellowship for scholarly achievement from Stanford. He researches internet-based market research methods, mathematical models of parallel and sequential prototyping, the economics of cost reduction, and models of mass customization. Dahan received a Bachelor's degree in Civil Engineering from MIT and an MBA from Harvard. He was national product manager for W.R. Grace and NEC until 1984, when he founded a computer networking company in Maryland, serving as CEO until the firm was acquired in 1993.

John R. Hauser is the Kirin Professor of Marketing and Head of the Marketing Group at M.I.T.'s Sloan School of Management where he teaches new product development, marketing management, and research methodology. He is the co-author of Design and Marketing of New Products and has published over sixty scientific papers. He has consulted for a variety of corpo-rations on product development, marketing research, and voice of the customer implementation. He is one of the founders of Applied Marketing Sciences, Inc His awards include the Converse Award for contributions to the science of marketing and the Parlin Award for contributions to marketing research. He has won numerous awards from the AMA and INFORMS for his scien-tific papers and his students have won major thesis awards including the Brooke's Prize, the Zannetos' Prize, the American Marketing Association dissertation award, and the INFORMS Bass Award. Current interests include metrics for product development, new methods for target-ing customer needs, and new applications of reinforcement learning to automate marketing tac-tics and product design.


Abstract

Communication and information technologies are adding new capabilities for rapid and

inexpensive customer input to all stages of the product development (PD) process. In this article

we review six web-based methods of customer input as examples of the improved Internet capa-

bilities of communication, conceptualization, and computation. For each method we give exam-

ples of user-interfaces, initial applications, and validity tests. We critique the applicability of the

methods for use in the various stages of PD and discuss how they complement existing methods.

For example, during the fuzzy front end of PD the information pump enables customers

to interact with each other in a web-based game that provides incentives for truth-telling and

thinking hard, thus providing new ways for customers to verbalize the product features that are

important to them. Fast polyhedral adaptive conjoint estimation enables PD teams to screen lar-

ger numbers of product features inexpensively to identify and measure the importance of the

most promising features for further development. Meanwhile, interactive web-based conjoint

analysis interfaces are moving this proven set of methods to the web while exploiting new capa-

bilities to present products, features, product use, and marketing elements in streaming multime-

dia representations. User design exploits the interactivity of the web to enable users to design

their own virtual products thus enabling the PD team to understand complex feature interactions

and enabling customers to learn their own preferences for new products. These methods can be

valuable for identifying opportunities, improving the design and engineering of products, and

testing ideas and concepts much earlier in the process when less time and money is at risk. As

products move toward pretesting and testing, virtual concept testing on the web enables PD

teams to test concepts without actually building the product. Further, by combining virtual con-

cepts and the ability of customers to interact with one another in a stock-market-like game, secu-

rities trading of concepts provides a novel way to identify winning concepts.

Prototypes of all six methods are available and have been tested with real products and

real customers. These tests demonstrate reliability for web-based conjoint analysis, polyhedral

methods, virtual concept testing, and stock-market-like trading; external validity for web-based

conjoint analysis and polyhedral methods; and consistency for web-based conjoint analysis vs.

user design. We report on these tests, commercial applications, and other evaluations.

THE VIRTUAL CUSTOMER PAGE 1


Introduction

New communications and information technologies such as the Internet, the World-Wide

Web (web), and high-speed, broadband connections are transforming product development (PD).

The PD process itself is transforming into an activity that is dispersed and global with cross-

functional PD team members spread across multiple locations and time zones and interconnected

through a services marketplace. For example, Wallace, Abrahamson, Senin, and Sferro [85] re-

port on a system that enables engineers to redesign critical components in weeks or even days –

redesigns that once took six months. These and other changes put an emphasis on fast and accu-

rate input from a variety of sources, including rapid input from customers [1, 13, 14, 20, 32, 52,

67]. At the same time, today’s spiral and stage-gate processes require input from customers it-

eratively at many times during the development process including the rapid evaluation of ideas

early in the process, the identification of important “delighter” features as the product concept is

refined, detailed measures of the importances of customer needs as the product is engineered,

and accurate evaluation of prototypes as the product nears pretest and test marketing [11, 12, 15,

16, 37, 52, 53, 66, 73, 77, 87].

While information technology transforms internal PD processes within firms, it also im-

pacts firms’ external interactions with potential consumers of new products. Customers’ broad-

band connections at home and work, combined with emerging Internet panels of willing

respondents, mean that PD teams can reach customers more quickly and, ultimately, less expen-

sively. Media rich computing and communication mean that product stimuli can include more

realistic virtual prototypes and more realistic product features. And powerful, server-based soft-

ware and downloadable applets mean that web-based methods can be more adaptive to customer

input and change questioning procedures on the fly.

In this article we review six web-based customer input methods. For simplicity, we call

this set of methods the “virtual customer.” Some of these methods simply move paper-and-

pencil or central-location interviewing methods to the web. Others exploit the new communica-

tions and computing power to provide capabilities that were not feasible previously. Each of

these methods has been implemented and pilot tested and some of the methods have been used to

design products that have now been launched. However, we caution the reader that web-based


methods of gathering customer input continue to evolve. We and other researchers continue to

test these methods in new applications and to explore new web-based methods to discover their

strengths and weaknesses. In some applications, the virtual customer methods will replace exist-

ing methods, but in most instances they will complement existing methods for expanded capabil-

ity.

We organize the article as follows. We begin with a discussion of the three dimensions

of web-based customer input. This structure is useful to (1) show how the web-based methods

complement traditional market research and (2) suggest how web-based research will evolve.

We then describe each of the six methods providing examples and, when they are available, ini-

tial applications and tests. Finally, we review how each method can be used in the various stages

of an iterative PD process. Demonstrations of these methods, open-source software, technical

reports, and theses are available at the virtual-customer website: mitsloan.mit.edu/vc.

Communication, Conceptualization, and Computation

Figure 1 depicts three capabilities of web-based customer input. The capabilities extend

and enhance the trends that we have seen over the past ten years as computer-aided interviewing

(CAI) has enhanced traditional telephone and central-location interviewing. The web has made

these capabilities more powerful and is putting these capabilities directly into the hands of the

PD team.

Communication includes much more rapid interaction not only between the PD team and

the respondents, but also between the respondents themselves. PD-team-with-respondent com-

munication reduces the time required to conduct studies, and enhances understanding of the re-

spondents’ task through interactive, hyperlinked help systems incorporated into the website.

With this rapid communication it is now theoretically possible to gather sophisticated market in-

formation in a few days rather than the 4-6 weeks that are typical with traditional methods. For

example, we completed a user design study of over 300 respondents over a weekend.

To get to customers quickly, both new and traditional market research firms are forming

panels of web-enabled respondents who can complete on-line tasks. National Family Opinion

Interactive, Inc. (NFOi) has a balanced panel of over 500,000 web-enabled respondents. Digital

Marketing Services, Inc., (DMS), a subsidiary of AOL, uses “Opinion Place” to recruit respon-

dents dynamically and claims to be interviewing over 1 million respondents per year. Knowl-

edge Networks has recruited 100,000 Internet enabled respondents. Greenfield Online, Inc. has


an on-line panel of 1.2 million households (3 million respondents). Harris Interactive, Inc. has an

on-line panel of 6.5 million respondents [4, 25, 56].

These market research firms are aware of the fact that the Internet is still diffusing and

are competing on ways to ensure representativeness of these panels. For example, NFO has had

fifty years of experience balancing their traditional panels and NFOi is using that same technol-

ogy to balance its Internet panel. DMS reports on 150 side-by-side tests of on-line vs.

phone/mail/mall interviewing and states that “a rather extensive body of comparability work

documents the consistent business direction finding [25].” Gonier [25] presents data that the

DMS respondents have demographics close to the US population and can be balanced to match

the US population. Knowledge Networks addresses representativeness by recruiting respondents

with random digit dialing methods and provides them with web access if they do not already

have it. In a more independent test, Willkie, Adams, and Girnius [86] conducted 50 parallel tests

and found a high degree of correlation between mall-intercept and web-panel respondents.

These panels also make it possible to gather customer input in multiple countries simul-

taneously. For example, Harris Interactive claims panelists in 200 countries and Greenfield On-

line claims panelists in 162 countries [56]. Our own experience with NFOi suggests that it is

relatively easy to field studies in multiple languages simultaneously. Although independent rep-

resentativeness tests are still rare, we can take confidence in the fact that firms such as Apple

Computers, Avon, Beecham, BMW, Hewlett Packard, IBM, Kodak, Microsoft, Pfizer, Procter &

Gamble, Ralston Purina, and Xerox now use these panels (www.nfoi.com,

www.greenfield.com). In fact, General Mills now claims to do 60% of their market research on-

line [50].

As of this writing, these panels have focused on the consumer market. Although Harris

Interactive does have a successful physician panel, recruiting has proven much more difficult for

business-to-business panels. To date, most of the business-to-business on-line interviewing has

required study-specific recruiting, thus mitigating some of the cost and time advantages. How-

ever, cost and time might decrease with experience and competitive pressure.

To help PD teams implement studies quickly, application service providers (ASP’s) are

developing web-based menu-driven systems by which teams can create customized surveys. For

example, Faura [23] demonstrates a system in which a PD team member need only visit a web-

site to choose the features and feature levels to be tested in conjoint analysis. The website then


sets up the web-page to which respondents can come, sets up the database, and provides analysis

summaries – all automatically. Faura’s system is only a proof-of-concept rather than a commer-

cial system, but other ASP’s, such as zoomerang.com, are now in common use for web-based

surveys, and Sawtooth Software, Inc. has recently announced commercial software for the design

of web-based interviewing systems.

The web also facilitates respondent-to-respondent communication that might also im-

prove the quality of information gathered, particularly for product categories (e.g., automobiles

and communication devices) in which customers may influence one another’s choices. Real-

time respondent-to-respondent communication can inform inter-subjectivity just as the wide-

spread availability of real-time stock market quotations informs individual traders about the state

of the financial world. PD teams can now observe respondent-to-respondent interactions to gain

insight into customer needs and better estimate a new concept’s potential. Although respondent-

to-respondent capability has always been possible in face-to-face interviews such as focus

groups, the web enables this communication to take place among larger numbers of customers

and web-based interviewing enables the PD team to gather this information more rapidly. Addi-

tionally, web-based methods such as the information pump and securities trading of concepts are

designed to be less susceptible to social influences than in-person focus groups.

There is, however, a downside to rapid communication. With customers providing feed-

back on-line from the comfort of their homes or workplaces, the alternative uses of their time are

high. Unlike in a central facility, web-based respondents are free to terminate the interview if

they are bored or if they do not feel that the incentives (if any) justify their time. It is more diffi-

cult for some web-based survey methods to obtain the same response rates as mail/phone/mall

interviewing. A web-based environment places a premium on interfaces that are interesting and

engaging and which gather information using as few questions as is feasible. It is not enough to

simply port existing methods to the web; they must be designed with the web in mind.

Conceptualization utilizes the graphic and audio capabilities of multimedia computers to

depict virtual products and product features. Concept evaluation has long been possible with

physical prototypes, but such methods are expensive and time-consuming [47, 55, 58, 65, 79].

With rich virtual prototypes, PD teams can test their ideas and preliminary designs earlier in the

process, well before physical prototypes are built. Although prior research has used virtual pro-

totypes and information acceleration in central-location interviewing [78, 81 and Sawtooth


Technologies’ multimedia Sensus capability, www.sawtooth.com], these capabilities are now

becoming available on the web. Further, new software and hardware is making the multimedia

prototypes easier to develop and more realistic. These interactive, media-rich depictions also en-

hance respondents’ understanding and enjoyment of the task. Conceptualization may include

multiple sensory inputs such as 2-D and 3-D visualization, interactivity, sound and music, and,

eventually, touch, smell, and even taste through peripherals that are now being developed. Even

for products or prototypes that exist in physical reality, virtual depictions have a cost and speed

advantage over physical prototypes.

Naturally, PD teams realize these advantages only if the data collected based on virtual

prototypes replicates that which can be obtained with physical prototypes, and only if web-based

interviewing replicates that which can be obtained with more traditional central-location inter-

viewing. Although capabilities will improve with further experience, the initial tests reported in

this article suggest that sufficient accuracy and reliability can be obtained.

Computation enables improvement over fixed survey designs by dynamically adapting

web-pages in real time, based on mathematical algorithms, while participants are responding.

For example, adaptive conjoint analysis (ACA) has long adapted paired-comparison preference

questions to each respondent based upon their answers to earlier questions [29, 41, 57, 62]. Not

only has ACA moved to the web, but more computationally intensive optimization methods (de-

scribed later) are being used to select stimuli. Even the multimedia stimuli themselves can be

created on the fly. Suppose that the PD team was considering 50 alternative features for a prod-

uct. Even with an efficient experimental orthogonal design for this 250 problem, the number of

stimuli that would need to be created would be huge. With today’s software tools that enable

layering of “puzzle pieces,” we can now create stimuli automatically as they are needed.

Real-time computation also enables stimuli to become dynamic, interactive, and more in-

formative. For example, instantaneous computation of price and performance as a function of

design choices provides key feedback during the user design process. In this way, the end-user

can better learn about tradeoffs and his or her personal preferences, thereby improving the accu-

racy of decisions about an “ideal design.”

Virtual Customer Methods

We now describe six virtual customer methods representative of the type of web-based

customer input systems that are evolving. We chose these six methods as representative of the


space of current virtual-customer methods. These methods differ in the extent to which the ques-

tion selection is fixed or adaptive and in the extent to which the method focuses on product fea-

tures versus fully integrated product concepts. See Figure 2. Each method has been

implemented in a working system and has been applied with realistic stimuli, some as part of

commercial PD processes. We provide perspectives on the advantages and challenges of each.

We invite the reader to combine aspects of these six methods and, perhaps, create new, custom-

ized methods just as product designers often select the best features from multiple concepts and

incorporate them into a final design (e.g., [61]). We also expect that each of these methods will

evolve with further experience and as the communication, conceptualization, and computational

capabilities of the web increase. We hope that refinements of the initial versions presented here

will fully empower cross-functional teams to explore multiple design solutions and tear down the

barriers between engineer/designers and end-users.

Figure 3 summarizes the six methods and highlights how each of the current implementa-

tions exploit communication, conceptualization, and computation. We begin with web-based

conjoint analysis (WCA) as an example of how traditional customer feedback systems are mov-

ing to the web.

Web-based Conjoint Analysis (WCA)

Conjoint analysis has been the subject of intense academic research for over twenty years

[cf. 30, 31]. Basically, in a conjoint analysis study, products or product concepts are represented

by their features, where each feature can have two or more alternative levels. The goal of the

study is to find out which features and feature levels customers prefer and how much they value

the features. For example, a new instant camera might be represented by features such as image

quality, picture taking (1-step or 2-step), picture removal method (motorized ejection or manual

pull), light selection method, and two styling attributes – opening (slide open or fixed) and styl-

ing covers (Figure 4). Other features such as picture size, picture type, camera size, battery type,

etc. might either be assumed constant among all concepts under study or might be the focus of a

separate study. The data collection and analysis procedures are many and varied. For example,

product profiles might represent a factorial design of the feature levels and the respondent might

be asked to rank order all profiles in terms of preference. Alternatively, respondents might be

presented with many groups of attribute bundles and asked to select one from each group, or,

they might be given pairs of concepts and asked to select between the two concepts. Hybrid


methods ask customers to rate the importance of the features directly and then update those im-

portance measurements with data from profile ranks, choices, or paired comparisons. All of

these methods work with either rank-order data or a rating scale that is designed to measure the

intensity of preference.

Although new analysis methods are being developed that exploit new computational al-

gorithms (described later), the primary focus in web-based conjoint analysis has been the user

interface. Among the challenges are (a) the limited screen “real estate” of most computer moni-

tors which constrains the number of profiles that can be viewed, (b) the limited time and concen-

tration that most respondents commit to the task, and (c) the fact that instructions and tasks must

be understood without the researcher present. On the other hand, the web offers multiple bene-

fits including: (1) enhanced stimuli that are visual, animated, interactive, and hyperlinked, (2)

flexibility to enable respondents to participate at their convenience from the comfort of their

homes or workplaces, and (3) the engaging ease and speed with which respondents can express

their preferences through simple clicking, without requiring typing. An effective user interface

exploits web-based benefits to address the challenges as they relate to: the respondents’ task, the

number of features, the number of levels, the number of stimuli, and the depiction of concepts.

We illustrate two web-based interfaces. The first collects paired-comparison data and the

second gathers ranks on full profiles of features. These methods are extendable to customization

with self-explicated importance ratings, other intensity measures, or choice-based tasks.

The paired-comparison study [51] explored the six features of an instant camera (Figure

4) targeted at preteens and teenagers. Because this was a pilot test of the method, the interface

was programmed in HTML specifically for this application. However, this study did demon-

strate that web-based interviewing could be used for products targeted at difficult-to-reach re-

spondents such as children.1

Because the concept of a postage-stamp size picture was relatively new at the time of the

study and because many of the features required visualization, the study began with interactive

screens that introduced the product and its features. For example, in Figure 5 respondents click

on any image to get a demonstration of the product’s use – say customizing your math textbook

with an image that illustrates how you feel about the subject. Applets enable respondents to ob-

serve picture quality, how the camera opens, how photos are ejected, etc. Post analysis sug-


gested that the children enjoyed the task (“kind of fun”) and found it to be about the right length

[51].

After the product- and feature-instruction screens, respondents completed the paired-

comparison task in Figure 6. The task was made easier for the respondent by animating the 9-

point scale and by making detailed feature descriptions or product demonstrations available with

a single click. While paired-comparison questions fit the need for clarity and limited screen size

quite well, pretests and prior studies suggested that they became monotonous after 10-15 ques-

tions [8]. Thus, the children were asked only eight paired-comparison questions. This limited the

data analysis to aggregate (segment-level) estimates of feature importance and concept share.

While this was sufficient for the application in which only six features (plus price) varied, it be-

came clear to both the firm and to us that new adaptive methods were necessary for more com-

plex problems. These methods are described later in this article.

The application was considered a success by the firm. The PD team felt that the data had

high face validity and internal consistency. Because the firm had previously relied on mall-

intercept interviewing, a parallel study was completed in which respondents were recruited in a

mall and brought to a central facility to complete the conjoint analysis tasks. The partworth es-

timates from the two studies were highly correlated (0.80 correlation significant at the 0.01

level). Despite some slight differences, the basic managerial message was the same and implied

the same camera design, thus suggesting that the more rapid web-based interviewing could sub-

stitute adequately for traditional mall-based interviewing. Furthermore, the percentage of re-

spondents who answered the survey completely without task neglect was 85% in the mall and

86% at home suggesting that the interface was engaging and that the task was not too onerous.

However, the percent of respondents who visited the website after being recruited was 38% sug-

gesting a need for improvement relative to the 50% that is typical for telephone interviewing [89,

90].2

The study identified at least one feature that was a “delighter” to teenagers, but not an-

ticipated by the adult PD team – removable styling covers. The camera was launched as the

“iZone Convertible Camera” with “fashion-forward faceplates” in multiple styles and colors

(www.izone.com). The study also identified features that were not important to children and

could be eliminated from the camera to keep the design within the price target. In particular, it


did not appear that teenagers valued a folding camera or one that ejected the pictures automati-

cally.

Full-profile Evaluation Interface for Web-based Conjoint Analysis (WCA)

The paired-comparison task is ideally suited for CAI. It underlies Sawtooth’s widely-

used version of ACA, and CAI conjoint examples with paired comparisons have been in the aca-

demic literature for twenty years (e.g., [35, 36]). However, ranks of full-profile concepts remain

the most common form of conjoint analysis among practitioners accounting for over 60% of the

applications [7, 46, 91]. Not surprisingly, ranking many concepts puts high demands on screen

real estate and requires a creative user interface. Such interfaces are still being developed and

refined; we present one that has now been applied for crossover vehicles, ski resorts, tape backup

systems, digital cameras, automobile telematics, pocket PCs, high-speed color printers/copiers,

and ultra-light portable computers. Respondents find the task intuitive, interesting, and easy-to-

complete.

We illustrate the task with “crossover vehicles” – car/trucks that combine the all-wheel

drive and height of sport utility vehicles (SUV’s), the amenities and ride of luxury cars, and the

interior flexibility of minivans. After much experimentation and pretest, we found that respon-

dents were most comfortable seeing no more than twelve stimuli per screen. One such design is

shown in Figure 7. (The squares in the upper left corner of each stimulus are color-coded to

match the high vs. low levels of the product features. We found that such visual cues help the

respondent complete the task more quickly.)

The orthogonal design for crossover vehicles consists of 12 profiles from a 27 factorial

design. For designs larger than twelve stimuli, this interface can be extended, up to the limit of

respondent fatigue, by displaying multiple screens of up to twelve profiles per screen. Each pro-

file “card” is an independent HTML file that is randomized on the screen. (Respondents see this

screen after first being introduced to the product category and the features in an interactive fash-

ion not unlike that described for the camera in Figures 4 and 5.)

Pretests suggest that ranking all twelve images on one screen is difficult for respondents. In-

stead, we evolved the following set of tasks.

• For each set of twelve stimuli, respondents click on those cards, in no particular order, that

they would be “likely to buy.” Clicked cards disappear from the screen.

• Respondents then click on cards that they would be “unlikely to buy.”


• Remaining cards are automatically added to a “not sure” group.

• Respondents then rank order stimuli within each of the “likely,” “not sure,” or “unlikely”

groups by clicking on profiles in order of preference. Each clicked profile disappears from

the screen, so respondents are always clicking on their most preferred remaining profile.

• The rare groups with more than twelve cards require scrolling within the browser window.

• To check for errors and to iterate if necessary, respondents are asked paired-comparison

questions that compare the least preferred “likely” profile to the most preferred “not sure”

profile, and the least preferred “not sure” profile to the most preferred “unlikely” profile.

• Finally, the “likely,” “not sure,” and “unlikely” groups are “stitched” together to create the

rank order of all stimuli for analysis.

In addition to the rank orders, this user interface also identifies “likely” and “unlikely” profiles

with which to estimate minimum utility cutoffs. Such screening has been shown to improve es-

timation accuracy [49].

Initial tests of this interface suggest strong internal consistency. Groups of students and

eBusiness executives yielded mean violated pairs (mean number of pairs of profiles ordered in-

consistently with the estimated utility function prediction) of only 2.7% of the possible pairs as

compared with the 12.6% that would result under a random ordering (n = 158 respondents in

four separate studies). Although the interface is promising as a means to port full-profile con-

joint analysis to the web, it is limited to six to ten features because respondents appear to have

difficulty making simultaneous evaluations of more than this number of features and due to

screen real estate constraints. Because conjoint analysis has a long history, we expect that the

reliability and validity of web-based methods will be refined to match that of central-facility

methods. We provide one external validity test of the paired-comparison interface when we re-

view the newer polyhedral adaptive conjoint analysis method in the next section of this article.

The paired-comparison and full-profile user interfaces represent two web-based conjoint

analysis applications that enable the PD team to get rapid feedback about feature importances

from customers. Both applications have high face validity and provide valuable insight for the

design of the product in question. However, to date, these interfaces are limited by potential re-

spondent wear out and, hence, have been applied to relatively small designs. These are certainly

not the only interfaces possible and, given the large academic and industry interest in conjoint

analysis, we expect these interfaces to be refined over the next few years. Such refinement


should soon make it feasible to use web-based hybrid designs that can deal with the fifty or more

parameters that are possible with central-facility interviewing (cf. [88]).

Fast Polyhedral Adaptive Conjoint Estimation (FastPace)

Concern about respondent burden in conjoint analysis is not new. As early as 1978,

Carmone, Green, and Jain [5, p. 300] cautioned that most conjoint applications required more

parameters to be estimated than the number of profiles that customers could rank comfortably.

Other researchers suggested that, due to respondent wear out, accuracy degrades as the number

of questions increases [2, 27, 28, 39, 44, 48, 49, 54, 72]. Over the past twenty years many re-

searchers have proposed methods to simplify the experimental design, simply the respondents’

task, eliminate profiles or features, and use hybrid methods that mix individual-level and seg-

ment-level data (cf. [26, 70]). In particular, adaptive conjoint analysis (ACA) has enjoyed wide

use. Green, Krieger, and Agarwal [29] claim that ACA has grown quickly to become one of the

most widely used conjoint analysis methods. ACA seeks to reduce the number of questions re-

quired by using respondents’ earlier answers to customize later questions [62].

In ACA, respondents first state the importance of each feature (self-explicated phase) and

then indicate their relative preferences between pairs of partial profiles (paired-comparison

phase). The resulting utility estimates are then scaled to predict choice based on respondents’

self-stated probability of purchase for several full product profiles (purchase intention phase). In

the adaptive phase (paired comparisons), profiles are chosen such that both profiles in a pair are

nearly equal in utility, subject to constraints that make the overall design as orthogonal as possi-

ble. ACA has proven accurate under the right circumstances, and the adaptive phase has proven

to add incremental information relative to the self-explicated phase of the interview [39, 40, 57].

In addition, Johnson [42] proposes that the accuracy of ACA can be improved by post-analyzing

the data with a hierarchical Bayes algorithm. See further discussion in Green, Krieger, and

Agarwal [29] who suggest when ACA is appropriate and when caution is due.

To date, although ACA is a CAI system, most applications have required a central facility

to which customers are recruited. Recently, Sawtooth Software introduced web-enabled ACA

and claims seventy-five applications in beta testing of their web-based interviewing system

which includes ACA as a tool (www.sawtoothsoftware.com). However, even with the adaptive

portion, ACA does not fully solve the need for a reduced number of questions – a need that be-

comes more acute on the web due to the need to hold a respondent’s attention. If a conjoint


analysis study requires p parameters to be estimated – e.g., for p features at two levels each –

then ACA requires approximately 3p questions: p self-explicated questions plus 2p paired-

comparison questions, as well as the purchase intent questions. While this is sometimes a dra-

matic improvement over non-adaptive methods, it might still be a large burden for the typical

web-based respondent.

Fortunately, new computation developments have the potential to improve adaptive con-

joint questioning for web-based respondents. In particular, a revolution in mathematical pro-

gramming begun by Karmarkar [43] in 1984 enables researchers to design robust heuristic

algorithms that obtain excellent approximations to complex computational problems. Most im-

portantly, these algorithms run extremely fast. These algorithms, coupled with today’s fast com-

puters mean that adaptive paired-comparison questions can be found such that they provide

conjoint-analysis estimates with fewer questions. In some cases, the self-explicated questions

can be skipped entirely and good approximations can be found with fewer than p questions.

While such estimates do not have the nice theoretical statistical properties of estimates based on

least-squares or maximum-likelihood estimation, there is some evidence that when respondent

fatigue is a concern, estimates based on fewer questions might actually be more accurate [74].

The heuristic algorithms are surprisingly accurate and hold promise when PD teams seek to iden-

tify quickly which features are among the most important. Hence, the fast polyhedral methods

are most useful in the early stages of PD when the team is trying to winnow the list of important

features of a new product in order to identify exciting new concepts.

We describe here the concepts underlying one such “interior-point” algorithm based on

proportional ellipsoids and the analytic center [24, 68, 69, 82]. Toubia, Simester, and Hauser

[74] propose that each respondent be described by a vector of the relative importances that he or

she ascribes to each of p features. If these importances are scaled between 0 and 100, then the

feasible set of relative importances is a hypercube in p dimensions. The trick is to ask questions

that shrink the feasible set of parameter values as quickly as possible. At any given point in

time, say after q questions, the best estimate for a respondent’s feature importances is then speci-

fied by the analytic center of the remaining feasible set.3 The exact algorithm (and how the au-

thors model measurement error) is beyond the scope of this article. However, the algorithm is

sufficiently fast that respondents experience minimal computational delays between the paired-

comparison questions.4


The algorithm was tested initially using Monte Carlo simulation of 1,000 respondents

each for ACA, an efficient fixed factorial design, and the authors’ algorithm, which they dub

FastPace (FP). In simulation, FP is more accurate than fixed designs for any number of ques-

tions up to 1.5 times the number of parameters and gets close to the “correct” answers in fewer

questions. For example, after only ten questions FP’s mean absolute error is only 46% higher

than that obtained with an efficient design of twenty fixed pairs. The comparison with ACA is

more complex because ACA requires p initial self-stated importance questions. However, in one

example the authors show that if the self-stated importances are relatively noisy, then FP can ob-

tain the same accuracy in ten paired-comparison questions as ACA obtains in twenty paired-

comparison questions plus ten self-stated importances. If the self-stated importances are not

noisy, then ACA is more accurate initially than FP, but a hybrid that incorporates self-stated im-

portances into the FP algorithm is even more accurate than ACA. The authors conclude that FP

is particularly promising when PD teams are limited to relatively few questions, when respon-

dent wear out is a significant concern, and/or when self-stated importances are noisy.

FP was then tested in a validation experiment by Dahan, Hauser, Simester, and Toubia

[17]. Respondents compared pairs of laptop computer bags using an interface similar to that in

Figure 6, with additional questions as required by ACA. The bags varied on nine features plus

price. Approximately one-half of the respondents were randomly assigned to an FP-based sur-

vey (n=162), approximately one-fourth to an ACA-based survey (n=80), and approximately one-

fourth (n=88) to a survey based on an efficient fixed design. After completing the survey and

then a filler task, the respondents were given the choice of five laptop bags that varied on fea-

tures and price. (The five bags were chosen randomly from a factorial design of sixteen bags.)

The choice was real – the respondents were given the bag they chose plus any change from $100.

Respondents ranked all five bags under the belief that they would be given lower choices if their

top choices were not available.

Table 1 reports the results of validity tests. The first sixteen paired-comparison questions

were chosen by the method being tested. Respondents then completed four additional paired-

comparison holdout questions, providing a test of internal validity. The external validity test

compared the ability of each method to forecast the respondents’ choices of bags. Since the

forecasts are based directly on the estimation of the importances of the products’ features, they

implicitly test the ability of the various methods to accurately assign importances to product fea-


tures. Based on this initial experiment, FP appears to be significantly better (0.01 level) than both

ACA and fixed designs for estimates based on eight questions and for estimates based on sixteen

questions. Furthermore, the eight-question FP results appear to do as well as the sixteen-

question ACA or fixed-design results. Further analysis suggests that these estimates can be im-

proved with at least two hybrid methods, one that combines self-stated importances with the FP

algorithm and one that uses an alternative ACA estimation algorithm.5 Based on these tests, the

new computational algorithms appear to hold promise for further developments that could enable

PD teams to test more features with fewer questions.

User Design (UD)

We now turn to the final feature-based method that complements WCA and FP. Both

WCA and FP exploit some of the web-based interactivity to provide estimates for each respon-

dent of the relative importances of product features. These data enable the PD team to forecast

customer reaction to any combination of product features, not just those tested directly. How-

ever, even with adaptive methods, the number of parameters that can be estimated is limited by

the patience of the respondents. If features have interactions, such as a respondent valuing cargo

capacity more in a seven-seat vehicle than in a five-seat vehicle, then even more questions must

be asked to identify relative importances, ultimately leading to respondent fatigue. This further

limits the number of features that can be tested. (For example, two independent three-level fea-

tures require four parameters but two interacting three-level features require eight parameters.)

User design (UD) sacrifices the generality of conjoint-based methods in order to handle

more features that might possibly interact. Because UD data gathers only the ideal feature com-

bination for each respondent, it does not have WCA’s and FP’s abilities to simulate how respon-

dents will react to any feature combination. However, UD can be used to determine which

features are most desired by customers, which features interact, and which feature combinations

are viewed as ideal by customers. In addition, the interface is enjoyable to the respondent and

relatively easy to implement. It has been applied to cameras, copier finishers, laptop bags, auto-

mobile telematics, toys (GI Joe and Mr. Potato Head), custom shotguns, and laundry products.

UD relies heavily on the web to exploit the proven ability of customers to design their own prod-

ucts. (See Urban and von Hippel [80] and von Hippel [83, 84] for examples of user input in the

PD process.)


Specifically, the web provides user interfaces that enable customers to select interactively

those features that they prefer in their ideal product. In many ways UD is similar to product

“configurators” used by websites such as Dell.com and Gateway.com, in which customers order

products by selecting features from drop-down menus. The key differences are (1) that UD uses

real and virtual features in a visually integrated format and (2) that the displayed product

changes interactively. These differences enable the PD team to determine which features to offer

customers. Van Buiten [75] describes such an approach applied to the design of future helicop-

ters, which improves on the usability of traditional configurators by enabling respondents to

drag-and-drop (DnD) their preferred features onto a design palette that illustrates the fully inte-

grated product.

For example, in Figure 8 respondents are shown the same six camera features from Fig-

ure 5. Respondents indicate which features they want in their camera by dragging features from

the “what you can buy” column to the “what your camera has” column. To remove features they

drag features from “what your camera has” to “what you can buy.” As respondents make these

choices, tradeoffs such as price, appearance, and performance are instantly visible and updated.

The respondents iteratively and interactively learn their preferences and reconfigure the design

until an “ideal” configuration is identified. The method can include full configuration logic, so

that only feasible designs can be generated – choices on one feature can preclude or interact with

choices on other features. For example, Figure 9 illustrates the use of UD in the design of a cop-

ier finisher. In this application, some features (C-fold and Z-fold) could not be chosen simulta-

neously. Beyond final feature choices, researchers observe click stream patterns and completion

times.

UD provides an engaging method of collecting data on customer tradeoffs. These data

can be used to narrow the set of features or determine which features should be standard and

which should be optional. The reduced set of features can then form the basis of a more exten-

sive conjoint analysis. While UD may be especially appropriate for “lead users” who are open to

exploring innovative solutions to address their acute needs [83, 84], we have found that the

method works well with “normal” users (even kids), once they have been briefed (via the web)

on the solution space and potential benefits of the product.

If PD teams are to use UD for the rapid screening of features, we would like to know

whether or not UD provides data that are consistent with the more intensive WCA and FP meth-


ods. Specifically, how well does UD identify important features and predict customer choice?

We begin by examining internal consistency with data from the camera WCA. Recall that only

eight WCA paired-comparison questions were asked per respondent in Figure 6, thus, we could

only obtain estimates of feature importance at the segment level. Because there were no signifi-

cant differences found among segments (male vs. female, pre-teen vs. teen) we compare popula-

tion-level estimates. In a parallel camera UD we recorded the number of customers who

included each of the six features in their ideal design. These percentages are shown on the hori-

zontal axis of Figure 10. To place the WCA estimates on the same scale we used logistic regres-

sion to map the partworth values and price to the choice percentages. These are shown on the

vertical axis of Figure 10. The correlation was quite high (0.91) and was significant at the 0.01

level.6

Although the camera UD-WCA comparison demonstrated consistency in a real product

that is now launched, it was limited to the aggregate level only. To test the consistency of UD

with WCA we completed two additional tests. One was based on the copier finisher in Figure 9

and another was based on the crossover vehicles in Figure 7. In each case we used WCA to es-

timate feature importances and price sensitivity for each respondent and used that data to predict

whether or not they would select that feature at the price shown in the UD. The WCA for the

copier finisher was based on an older interface similar to that used in virtual concept testing

(Figure 12). Respondents found this interface cumbersome for WCA and felt that this interface

overemphasized price. This led to the improved interface that was illustrated with crossover ve-

hicles (Figure 7). Thus, we were not surprised when the new interface was more consistent with

UD than the old interface. In particular, the older WCA was able to predict feature preference

correctly for 61.3% of the respondent-feature combinations (n=245 respondents x three features).

This improved to 66.0% when we readjusted overall price sensitivity with a logit model. How-

ever, with the newer interface the ability of WCA to predict feature preference improved to

73.1% (n=130 respondents x six features) without any adjustments. All of these predictions are

significantly higher than random at the 0.01 level.

We also examined the consistency of UD and WCA by using feature importances from

WCA to estimate a rank ordering of all potential UD combinations of non-price features, with

price a function of the other features. In the crossover vehicle example, UD yielded sixty-four

possible vehicle designs (26 possible configurations of six features at two levels each). Figure 11


reports the percentile rank of the UD selection for each respondent – 60% of the respondents

configured a vehicle that was in their top decile as predicted by WCA; 85% of the configurations

were in the top quartile.

Firms and researchers are just beginning to experiment with UD as a PD tool. Because

respondents find the interface easy to use, enjoyable, and fast, UD has the potential for screening

large numbers of features while highlighting interactions. For example, a UD for laptop bags

highlighted that logos were more likely to be preferred on bags that were offered in respondents’

school colors and that those respondents who chose cell-phone holders were more likely to

choose a PDA holder. In our applications we have assigned fixed prices to each feature, but

prices are easily randomized to enable measurement of price sensitivity. Liechty, Ramaswamy,

and Cohen [45] demonstrate one such approach in the context of a web-based Yellow Pages ser-

vice, and show how multiple UD exercises allow estimation of part worths at the individual

level.

The UD interface is also beginning to be used by manufacturers who sell mass-

customized goods over the web. One example is the website used by a laptop computer bag

manufacturer, Timbuk2.com. UD capability also opens new research opportunities for academ-

ics and new persuasive tools for marketing professionals. For example, Cattani, Dahan and

Schmidt [6] employ data from the laptop bag example to optimize mass customization. Park,

Jun, and MacInnis [59] demonstrate that customers arrive at different “ideal configurations” de-

pending on whether they are asked to add options to a base model or subtract options from a

fully loaded model. As these phenomena are better understood, site designers might enhance

sales effectiveness with the initial configuration of a UD website (in the case of mass-customized

e-commerce). This developing research also cautions market researchers that initial feature lev-

els that are presented to customers as defaults could influence measures of customer interest in

features.

Virtual Concept Testing (VCT)

Not all products can be completely decomposed into features. For example, while the

WCA in Figure 7 is useful to gain an understanding of how consumers value features in cross-

over vehicles, we would not expect those six features to fully describe a crossover vehicle. Styl-

ing is clearly important, as is brand and the manufacturer’s reputation for reliability and service.


Because holistic descriptions are critical to ultimate customer purchase decisions, PD teams of-

ten need to move beyond feature-based methods, especially later in the PD design process.

In virtual concept testing (VCT), respondents view new product concepts and express

their preferences by “buying” their most preferred concepts at varying prices. These choices are

converted into preferences for each concept by conjoint-analysis-like methods in which the rank-

order selections are explained with the two variables, price and concept, as in Dahan and Srini-

vasan [19]. The interface is illustrated in Figure 12 where each of eight crossover vehicles are

represented by brand name, pictures, and ratings on seven features. The respondent decides se-

quentially which concept they would buy at each of three prices, $25K, $35K and $45K. Be-

cause this method has already been published in the Journal of Product Innovation Management

we refer the reader to Dahan and Srinivasan [19] who demonstrate that VCT preferences are

highly correlated with concept tests based on physical prototypes. We replicated their approach

with eight crossover vehicles using three independent groups of respondents, two student groups

(n=43, 49) and a group of eBusiness executives (n=42), using the VCT task in Figure 12. The

forecast market shares had high reliability (Cronbach’s α = 0.95) for both first-preference shares

and for shares of the top three vehicles.

Our experience suggests that VCT complements WCA, FP, and UD. While WCA, FP,

and UD help the PD team identify the most important features, VCT enables evaluation of full

concepts, complete with pictures and other multimedia representations. We expect VCT to grow

in power and applicability over the next few years. With further development of multimedia

concept representation, more realistic and practical CAD renderings, and increased Internet

bandwidth, VCT methods have the potential to reduce the cost and time devoted to concept test-

ing and/or to increase the number of concepts that can be tested earlier in the PD process.

Securities Trading of Concepts (STOC)

We now review two methods that exploit the web’s ability to enhance communication

among customers and measure the preferences of a group of respondents. By structuring incen-

tives carefully so that customers act in their own best interests, one method (Securities Trading

of Concepts, STOC) uses the computational capability of web-based servers to monitor customer

interactions in a manner that attempts to reveal customers’ “true” preferences. Another method

(the Information Pump, IP) focuses on the language that respondents use to evaluate concepts


and features and, hence, provides an interesting complement to voice-of-the-customer methods

[33].

These interactive, incentive-compatible “games” have the potential to address the criti-

cisms of response biases and demand artifacts in survey research [63, 64]. Further, by observing

customer-to-customer interaction, these methods might extend virtual customer methods to those

products for which customers may be influenced by others’ opinions and choices – an externality

that is not easily accounted for with traditional concept testing methods. Both methods are rela-

tively new and, as such, we cannot yet report the same level of reliability and validity testing that

is available for the customer-feedback methods. Instead, we present both methods as examples

of the new ideas emerging from research on web-based customer-to-customer interaction.

The STOC method sets up a market in concepts through which “traders” reveal market

preferences as they buy and sell securities in a free market. A system implemented by Chan,

Dahan, Lo, and Poggio [9] uses fifteen or more respondents who simultaneously log onto a se-

cure website to engage in a trading game.7 Traders (respondents) are not asked their preferences

directly. Rather each trader is told to maximize the value of his or her portfolio of concepts.

Traders whose portfolios have higher values at the end of trading receive higher rewards.

The trading begins with an introduction to the product concepts (securities) where prod-

uct diagrams, photos, performance ratings, and textual information are provided in a web-based

interactive format. Figure 13 provides two examples – bike pumps and crossover vehicles. Af-

ter the securities briefing, traders are introduced to the STOC trading user interface in Figure 14.

It includes a buy-and-sell order entry form in the upper right, transaction monitoring in the center

right, a portfolio summary in the lower right, updated prices, spreads, and volumes in the lower

left, and a stock-by-stock graphical history in the upper left. This interface simulates the capa-

bilities available to Wall Street traders. Stock prices are strictly determined by exchanges be-

tween buyers and sellers. If the market is efficient, these valuations will depend upon traders’

personal evaluations of the securities, their expectations of others’ valuations, and the current

price of each stock. The innovation here is that the securities represent competing concepts

within a product category, similar to the Iowa Electronic Market (www.biz.uiowa.edu/iem/) in

which securities represent political candidacies and the Hollywood Exchange (www.HSX.com)

in which securities represent individual movies, actors, and directors. STOC uses the price

mechanism to rapidly disseminate preference information to enable the “market” to value win-


ning and losing product concepts. STOC builds on the IEM and HSX approaches, adding the

important element of virtual concepts including those that do not currently, nor might ever, exist.

In an initial test of the STOC method, we compared the outcomes of several trading game

experiments for a specific set of products with the outcomes of more traditional concept-testing

methods for those same products. Specifically, nine portable bicycle pump concepts from Dahan

and Srinivasan [19] were traded in two STOC games. The outcomes are plotted in Figure 15.8

Although the original preferences are based on a large sample survey of west coast students and

the STOC (median) prices are based on a smaller sample of east coast students two years later,

the top three “winners” are consistent across methods. The correlations between preferences and

STOC median prices are 0.88 and 0.82, both of which were significant at the 0.01 level. STOC

was then replicated using crossover vehicle concepts with two MBA student groups (n=43, 49)

and a group of eBusiness executives (n=42). The market shares, as forecast using the STOC me-

dian price and the STOC volume-weighted average price, were reliable (Cronbach’s α of 0.85

for each measure separately; 0.94 for the combined measures). Although no external measure of

market share was available, the shares forecast by STOC correlated well with first preference

shares (0.74, 0.01 level).

The potential advantages of STOC are (1) its ability to measure preference in situations

where one consumer’s preference depends upon the “market’s” preference (e.g., products in

which fashion and styling are important), (2) an ability to gather opinions quickly from custom-

ers through an enjoyable “game” experience, (3) incentive compatibility, and (4) several “price”

measures indicating each concept’s relative strength. Initial tests suggest that securities trading

can be taught to college-educated respondents quickly and naturally. However, STOC needs fur-

ther testing prior to full-scale adoption. In particular, the authors plan further usability testing

with a broader group of respondents and reliability and validity testing beyond that suggested by

Figure 15. This testing should isolate the “price” measures that are most predictive of ultimate

market shares. Experiments to date suggest that the closing prices and the maximum prices are

subject to manipulation by experienced “gamers.” In contrast, median, minimum, and volume-

weighted-average prices appear to be more accurate and robust predictors. Other experiments

will vary the information given to the “traders.” For example, the traders can be given informa-

tion from previous STOC tests and/or prior customer-feedback tests such as WCA, UD, FP, or

VCT.


The Information Pump (IP)

Most of the research on web-based methods has focused on the importance of alternative

product features and on concept evaluation, but the ability of the web to enhance customer-to-

customer communication can also be used to learn the voice of the customer in new and creative

ways. Prelec’s [60] information pump (IP) is a web-based customer input method that is focused

on the fuzzy front end of product development when the PD team is trying to understand the vo-

cabulary and descriptions that customers use for both existing products and new concepts. The

IP is, in essence, a virtual focus group but with some interesting twists based on the computa-

tional capabilities of today’s web interfaces. In particular, the task and the incentives in the IP

are fine-tuned so that the respondents think hard and provide honest answers.

The initial applications of the IP have been in the context of concept tests – respondents

are presented with virtual concepts, often with multimedia demonstrations, and are asked to de-

scribe these concepts. There are three roles in the “game” – encoder, decoder, and dummy. The

encoders and decoders see the concept but the dummy does not. The dummy remains the

dummy throughout the game, but the other respondents cycle through the roles of encoder and

decoder. Encoder/decoders each see the same basic concept, but are given different photographs

or renderings of the same concept. This way, when they communicate, they are forced to com-

municate about the fundamental characteristics of the concept, such as “the concept is a car for

young people,” rather than superficial features, such as “the car is in the middle of the photo.”

In any given round of the game, the encoder offers a true/false statement about the con-

cept, and states whether the statement is true or false. For example, the encoder might state that

a concept car is “good for city driving” and that the answer is “true.” The decoders then state

whether they perceive the statement as “true” or “false” and indicate their confidence in their an-

swer. If the concept really is “good for city driving” compared to an average automobile, then

the decoders will answer true with high confidence. The dummy views the statement (but not

the concept) and guesses the answer to the question. The dummy may or may not be able to

guess the answer correctly and may or may not be confident in his or her answer. If the state-

ment does not discriminate among cars (“has four wheels”) or if the statement is redundant with

previous statements (“an urban vehicle”), then the dummy can guess the answers as well as the

decoders. If the statement accurately describes the concept (i.e., is clearly true or false) and if

the statement provides a new and different description relative to previous statements in the


game, then the decoders will be able to figure out the answer better than the dummy, and with

higher confidence. To encourage truth telling, the decoders are rewarded on the accuracy of

their answers. They are rewarded more if they are more confident. To encourage the dummy to

think hard, the dummy is also rewarded on the accuracy and confidence of his or her answers. To

encourage the encoder to generate non-redundant, descriptive statements, the encoder is re-

warded on the accuracy and confidence of the decoders’ answers relative to the accuracy and

confidence of the dummy’s answers. Detailed rules of the game, an example reward structure,

and sample applications are available on the virtual customer website.

Figure 16 illustrates a typical user interface. A discussion log keeps respondents in-

formed of others' reactions and reinforces the rewards of the game. The specific reward structure

and the psychology behind the reward structure are based on the theories of truth-inducing loga-

rithmic scoring for non-zero-sum, non-cooperative games. They are beyond the scope of this

article but contained in Prelec [60].

The novel aspect of the scoring system is that the IP rewards participants for the quality

of the questions that they contribute to the exercise. A “good” question, according to the scoring

system, satisfies two criteria. First, it identifies something distinctive and descriptive about the

concept presented. Second, it is a new contribution to the discussion about this particular con-

cept. Questions that merely reformulate information contained in earlier items will not be re-

warded. As the game progresses the list of statements grows – each statement adds a new and

different perspective on the concept. Encoders have strong incentives to express needs clearly,

potentially making the IP effective at eliciting difficult-to-articulate needs and identifying re-

spondents who are skilled at doing so. Decoders have strong incentives to answer truthfully

about their perceptions of the product, thus making the IP an interesting new way to elicit re-

spondents’ true perceptions of concepts.

The IP has been pilot tested with concept cars and visual advertising materials and has

been benchmarked against a control procedure, which has the same “look and feel” as the infor-

mation pump, but without the interactive scoring system. Early indications suggest that the IP

provides customer statements that independent judges evaluate as more creative [60]. Currently,

the IP is limited by its need for respondents to play the game simultaneously, however, work is

underway to develop an asynchronous version in which respondents can visit a secure website at

their own convenience over the course of a study.


Virtual Customer Discussion

Web-based interviewing is a relatively new development that has the potential to trans-

form the way PD teams gather information from and interact with customers. It relies on ad-

vances in communication, conceptualization, and computation that increase the effectiveness and

efficiency of linking the voice of the customer directly to the capabilities of the PD team. How-

ever, there are many challenges to overcome. Like other disruptive technologies, the initial ap-

plications may not perform as well on traditional measures as do existing methodologies [3, 10].

Initially, PD teams will have to make tradeoffs; the old and the new will co-exist, with each be-

ing used for its unique advantages. However, as more researchers and more firms evolve web-

based customer input methods, we expect the weaknesses to be overcome and the strengths to

improve. We expect web-based interviewing soon to become an important paradigm for fulfill-

ing many of the customer-input requirements of the PD team.

While virtual customer methods may be used at every stage of product development, not

every method will be used at every stage. Figure 17 is based on our early experiences and is one

example of how the six methods might be used synergistically throughout the PD process. The

“PD funnel” in the center of Figure 17 is an abstract representation of the stages of PD as prod-

ucts move from ideas, to concepts, to design & engineering, to testing, and to launch. The ovals

in the funnel represent products that are winnowed, refined, and improved at each stage based on

customer input and other analyses. The four groups of products separated by dotted lines ab-

stract the concept of parallel development and product-platform development. For simplicity,

Figure 17 has the look and feel of a stage-gate process, but the applicability of virtual customer

methods is equally as strong for the new spiral PD processes.

The IP’s strength is its ability to gather the language of the customer, including features

and needs that are difficult for customers to articulate. One use is to identify opportunities and

ideas and to focus engineering teams on customer needs as seen through the lens of the custom-

ers’ language. Similarly, FP can be applied early in the PD process. Its strength is the ability to

screen large numbers of potential product features quickly. Because reasonable estimates can be

obtained with fewer questions than there are unknown parameters, the PD team can trade off a

small amount of accuracy for the ability to direct design attention toward a small, high-leverage

set of product features.


As the product moves from concept generation to design & engineering, the PD team

needs more accuracy and a deeper understanding of the tradeoffs that customers make when

evaluating products. Here WCA shines. The methods are built upon over twenty years of con-

joint-analysis research and application. The new interfaces rely on proven estimation methods

while bringing advanced conceptualization to virtual features so that they might be tested earlier

in the process and with greater speed. UD complements conjoint analysis by providing a means

by which customers design their own products. UD is particularly suited to products where the

features interact and where a conjoint-analysis application would need a large, complex experi-

mental design to estimate the interactions. In such situations, the PD team might be willing to

sacrifice the ability to measure detailed feature importances for each respondent. UD is also

suited to instances where customers need to learn their own preferences for really new products,

and might even be used as a training step prior to WCA or FP.

Once fully integrated product concepts are “developed,” they need to be tested. Here the

web brings a greater ability to evaluate multiple virtual concepts quickly. VCT enables the PD

team to get rapid and inexpensive feedback on the Product (with a big P) that includes descrip-

tions of the product and its features, illustrations of the product in use, and marketing elements

such as brochures, magazine articles, advertisements, and simulated word of mouth. In the early

1990s, virtual Product testing relied on expensive clinics in which customers were brought to a

central location and shown video tapes and other media [55, 58, 65, 79]. Such clinics often cost

hundreds of thousands of dollars. In the mid 1990s, virtual Product testing moved to computer-

based methods called information acceleration. However these, too, were expensive and difficult

to implement [78, 81]. As web access and web panels improve, web-based VCT promises to re-

duce these costs dramatically and to reduce time delays from weeks (or months) to days. New

software tools are making development less expensive, broadband communications are making it

feasible to stream multimedia experiences to customers, and pre-recruited panels (for consumer

goods) are making it quick.

STOC provides an alternative concept screening method, especially when the PD team is

dealing with a product in which customers’ preferences might depend upon what other customers

prefer (e.g., a fashion watch or personal communication device). However, while STOC pro-

vides reliable estimates, it is too early to tell whether STOC will realize the external validity of

more proven concept-testing methods.


The six virtual customer techniques reviewed in this article are just of sampling of the

methods that will evolve as information and communication technologies advance. For example,

Urban [75] is experimenting with virtual engineers that can “listen in” to customers as they

search the web for products to buy. His early work with truck purchasing is promising.

What is clear, however, is that the new information and communications technologies are

expanding the efficient frontier of the accuracy vs. cost/time tradeoff. In many situations, web-

based methods are cost efficient and their lower entry barriers put their capabilities directly into

the hands of the PD team. A day might come when conducting virtual customer tests is almost

as common as performing “what if” analyses with spreadsheet software.

Besides bringing more customer input to the PD process, virtual customer methods might

encourage a greater number of concepts to be explored and tested with customers. Srinivasan,

Lovejoy, and Beach [71] suggest further that PD teams will undertake more parallel concept test-

ing prior to “freezing” the design of a new product. Dahan and Mendelson [18] quantify the ar-

gument and suggest that under certain distributions of profit uncertainty, the optimal number of

concepts to be explored grows dramatically as (1) the cost per test declines and (2) when the up-

side profit opportunity declines at a slower-than-exponential (i.e., “fat-upper-tailed”) manner.

Current virtual customer methods have their weaknesses. They rely on virtual prototypes

rather than physical prototypes; software development is still embryonic, often requiring custom

programming for each application; panels are still being developed and their representativeness

is still being tested; and experience with the methods pales compared to experience with tradi-

tional methods. Initial tests suggest high face validity and good internal validity, but only WCA

and FP have been subjected to tests of external validity. Nonetheless, we are optimistic that

these challenges will be overcome by the product-development community and that virtual cus-

tomer methods will emerge as an integral component in the practice of product development.

THE VIRTUAL CUSTOMER, REFERENCES PAGE R1

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THE VIRTUAL CUSTOMER, TABLE AND FIGURES PAGE F1

Table 1. Validity Tests

Efficient Fixed

Designs

Adaptive Conjoint

Analysis (ACA) FastPace

Correlation with choice between holdout paired-comparisons (internal validity)

Based on 8 questions – 0.53 0.71

Based on 16 questions 0.73 0.61 0.81

Correlation with actual choice of product (external validity)

Based on 8 questions – 0.45 0.60

Based on 16 questions 0.54 0.52 0.68


Figure 1: Virtual Customer Research Exploits Three Dimensions of the Web

Communication Slow, sequentialcommunication

Fast, simultaneous communication

Capabilitychallenges

Web capabilities

Concepts Verbaldescriptions

Rich media

Computation Fixeddesign

Adaptive response


Figure 2: Six Representative Virtual Customer Methods

Adaptive DesignFixed DesignF

eatu

reB

ased

Inte

grat

edC

once

pts

Web-basedConjointAnalysis(WCA)

UserDesign(UD)

Fast PolyhedralAdaptiveConjoint

Estimation(FastPace)

VirtualConceptTesting(VCT)

InformationPump(IP)

SecuritiesTrading

of Concepts(STOC)


Figure 3: How Virtual Customer Research Exploits Web Technology

Method Description of

Respondents’ Task User

Interface Com

mun

icat

ion

Con

cept

ualiz

atio

n

Com

puta

tion

WEB-BASED CONJOINT ANALYSIS

(WCA)

Sort attribute-bundles by clicking on cards. To reduce the number of stimuli per screen, respondent pre-sorts into 3 piles.

1

6 2

3 5

4

4 44 4

FAST

POLYHEDRAL

ADAPTIVE

CONJOINT ESTIMATION

(FP)

Paired comparisons of attribute bundles. Respondent clicks radio buttons to express relative prefer-ence between two stimuli.

A B

o o o o o o o o o

attrib 1 attrib 2 attrib 3

4 4 44

USER DESIGN

(UD)

An “ideal” product is configured using visual drag-and-drop. Re-spondent trades off features against price or performance.

4 44 4

VIRTUAL CONCEPT TESTING

(VCT)

“Buy” from among competing concepts based on price and me-dia-rich, integrated concepts. Ana-lyzed as a two-attribute conjoint study.

4 44 4

SECURITIES TRADING OF

CONCEPTS (STOC)

Each product concept is repre-sented by a “security” and is bought and sold by respondents in-teracting with one another. Con-cepts can be richly depicted.

A

B

C

BUY

SELL

44 4 4

INFORMATION PUMP

(IP)

Players formulate questions about product concepts and guess how others will react to their questions. Fine-tuned so respondents think hard and tell the truth.

Do you think thatthe concept is…?

1 2 3 4 5Others will say…

TRUE FALSE

Do you think thatthe concept is…?

1 2 3 4 5Others will say…

TRUE FALSE

44 4 4


Figure 4: Six Attributes of an Instant Camera

LightSelection

PictureEjection

PictureTaking

StylingCovers

Slides Open Image Quality


Figure 5: Description of the Postage-Stamp-Size Instant Camera Category


Figure 6: Paired-comparison Task for an Instant Camera


Figure 7: Web-based Conjoint Analysis of Crossover Vehicles (rank order task)


Figure 8: User Design of an Instant Camera


Figure 9: User Design of a Copier Finisher via Drag-and-Drop (DnD)


Figure 10: Comparison of Camera Feature Shares from WCA and UD

0%

10%

20%

30%

40%

50%

60%

0% 10% 20% 30% 40% 50% 60%

User Design

Web

-bas

ed c

on

join

t an

alys

is


Figure 11. Respondents Use UD to Select Near-ideal Configurations

0%

10%

20%

30%

40%

50%

5% 10%15%

20%25%

30%35%

40%45%

50%55%

60%65%

70%75%

80%85%

90%95%

100%

Precentile of Rank of Chosen Combination

Per

cen

t o

f re

spo

nd

ents


Figure 12: Virtual Concept Test of Crossover Vehicles


Figure 13: Information Provided about Bike Pump and Crossover Vehicle “Securities”


Figure 14: STOC Trading User Interface


Figure 15: Comparison of STOC Prices and Concept Preferences

Skitzo SilverB

ulletEpic R

imG

ripG

earheadTR

S 2w

isterG

ecko C

yclone

Concept evaluation

1st STOC price

2nd STOC price0%

10%

20%

30%

40%


Figure 16. User Interface for the Information Pump


Figure 17: Example Virtual Customer Use at the Stages of Product Development

Opp

ortu

nity

Iden

tific

atio

n&

Idea

Gen

erat

ion

Concepts Design &Engineer

Testing Launch

Web-basedConjointAnalysis(WCA)

UserDesign(UD)

Fast PolyhedralAdaptiveConjoint

Estimation(FastPace)

VirtualConceptTesting(VCT)

SecuritiesTrading

of Concepts(STOC)

InformationPump(IP)

THE VIRTUAL CUSTOMER, ENDNOTES PAGE E1

Endnotes

1 Because of legal and moral requirements, we obtained parental permission for all interviews.

2 In 1999 some respondents were lost because they did not have Java script capability as required

by the camera website. Under today’s conditions, where Java capability is almost universal, this

response rate would have been 41-42%. 3 Selecting the center of the set is not unlike using equally weighted importances as a null hy-

pothesis. This is not an unreasonable null hypothesis given the proven robustness of the linear

model [21, 22, 34, 38, 54]. 4 For a demonstration with a 10-parameter problem and for software to implement the algorithm

see the virtual customer website. 5 Metric conjoint analysis (regression) cannot obtain estimates with fixed designs of eight ques-

tions because there are more parameters than there are degrees of freedom. For this product

category, features were easily separable and, hence, self-stated importances did well. Thus, the

FP and ACA algorithms that placed more emphasis on self-stated importances were able to im-

prove predictions. However, simulations suggest that this may not apply to all product catego-

ries. 6 The correlation is based on the six features; the underlying data is based on 75 respondents. 7 Chan, Dahan, Lo, and Poggio [9] speculate that STOC requires at least fifteen respondents for

the market to work well. However, STOC has been run with almost fifty respondents simultane-

ously. In theory, the software could handle thousands of respondents simultaneously.

8 For ease of reference, the STOC prices in Figure 15 are scaled to preferences via regression

(adjusted R2 of 0.71 and 0.64, respectively). This scaling does not affect the observed correla-

tions. The concept evaluation comes from Dahan and Srinivasan’s VCT [19].

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